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# Data Scientist

- **Company:** spotixx GmbH
- **Location:** Frankfurt am Main, Germany (Remote available)
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Amazon Web Services, Microsoft Azure, Fraud Prevention and Detection, Python (Programming Language), Machine Learning, Standard Sql, Transaction Data, Feature Engineering, Large Language Models, Information Technology
- **Published:** September 16, 2026
- **Apply:** https://www.careerjet.de/jobad/de6131fdfe51992baa1e33768a6c7953bc

## About the Role

* Degree in Data Science, Computer Science, Statistics, Mathematics, or a related field
 * 2-3 years of hands-on experience in machine learning and applied data science, ideally in fraud detection, banking, or financial services
 * Strong proficiency in Python and SQL
 * Deep understanding of classification and anomaly detection algorithms, with the ability to select, tune, and evaluate models using appropriate metrics and validation strategies
 * End-to-end ownership: from exploring high-dimensional data and building models to visualizing results, deploying to cloud platforms (GCP, Azure, or AWS), and managing the full model lifecycle
 * Experience with LLMs and agentic workflows
 * Familiarity with model explainability tools such as SHAP and model cards
 * Structured, self-directed working style and strong communication skills
 * Fluent in English; professional working proficiency in German, including client-facing technical discussions

## Description

We are looking for a hands-on Data Scientist who is passionate about applying machine learning to real-world financial crime problems. You enjoy working with complex transactional data, take ownership of models from idea to production, and care about building solutions that are not only accurate but also robust, explainable, and scalable. You are comfortable working in a fast-growing scale-up and collaborating closely with engineers and product teams.
Your responsibilities

 * Develop and improve ML models for fraud detection
 * Work on automated rule discovery and optimization for fraud detection
 * Explore transactional and behavioral data to identify suspicious patterns
 * Own feature engineering, model training, validation, and monitoring
 * Take models from prototype to production
 * Communicate results clearly to technical and business stakeholders


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